{
  "abstract": "Early risk stratification of COVID-19 patients at admission informs triage and resource allocation, particularly in low- and middle-income settings where intensive-care capacity is constrained. Most published models are not interpretable, rarely externally validated, and developed almost exclusively in high-income cohorts. We developed an interpretable random-forest classifier for three-class COVID-19 severity (Mild: not ventilated, survived; Severe: ventilated, survived; Fatal: died) using only data available within 24 hours of admission in 321 hospitalised PCR-confirmed COVID-19 patients in Pakistan. From 2,278 raw variables we retained 20 predictors by clinical screening and permutation-importance selection, with leakage-controlled multiple imputation by chained equations and SHapley Additive exPlanations (SHAP) for interpretation. A 15-feature variant was evaluated in two independent public cohorts: the Chinese iCTCF cohort (n = 894) and an Italian complete-blood-count cohort (n = 1,218). The development model achieved a macro F1 of 0.55, identifying the Fatal class most reliably (F1 0.67, recall 0.73) and Mild cases least well. SHAP identified a parsimonious set of admission-time predictors (oxygen saturation, lactate dehydrogenase, urea, C-reactive protein, age and radiographic pneumonia), including a raised urea-to-creatinine ratio in fatal cases most consistent with dehydration (median urea 80 vs 50 mg/dL, fatal vs mild; p < 0.001). Externally, the model retained useful discrimination in the Chinese cohort (mortality AUC 0.82 in 719 patients with recorded outcomes; severe-or-worse AUC 0.75; macro F1 0.50) but only modest discrimination in the Italian cohort (AUC 0.62, 95% CI 0.56-0.68), which we treat as a feature-availability stress test rather than a second validation. Cross-cohort SHAP rankings correlated strongly in China (Spearman {rho} = 0.91) and moderately in Italy ({rho} = 0.76), where most laboratory predictors were imputed and carried no information beyond the complete blood count; discrimination tracked how many predictors were actually measured. The admission-time signature converges with established markers of hypoperfusion and inflammation.\n\nAuthor summaryWhen patients arrive at hospital with COVID-19, risk must be assessed immediately to direct limited resources to those in need, particularly where intensive-care capacity is scarce, as in Pakistan. Most existing computer-based tools come from developed countries, act as \"black boxes\", and are rarely tested elsewhere. We built a model using first-day admission data from 321 patients at a hospital in Lahore, Pakistan. It identifies patients who later died best, and milder cases least well. The strongest markers were blood oxygen, lactate dehydrogenase, urea, C-reactive protein, age and chest-imaging evidence of pneumonia, together pointing to a coherent picture of pulmonary impairment, inflammation and circulatory stress. In fatal cases urea was markedly raised while creatinine rose only modestly, a pattern that most likely reflects dehydration. We then tested the same model on independent Chinese and Italian cohorts: it transferred well to China but lost accuracy in Italy, where only a basic blood count was available. The findings support an interpretable Day-1 prognostic tool whose useful range is set largely by which laboratory tests are available rather than by geography. Because these markers reflect general inflammation rather than anything unique to COVID-19, the approach may extend to other pneumonias.",
  "abstract_shown": true,
  "affiliations": [
    "Lahore University of Management Sciences"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "T. Jaffer"
    },
    {
      "name": "S. Aslam"
    },
    {
      "name": "N. Javaid"
    },
    {
      "name": "A. Khaliq"
    },
    {
      "name": "E. Tariq"
    },
    {
      "name": "T. Mustafa"
    },
    {
      "name": "M. A. Zaffar"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Mirza"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/26mcv721rd1wa.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.01.26364553",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.01.26364553v1.full",
  "id": "26mcv721rd1wa",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.01.26364553v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2725|COVID-19 Clinical Research Studies"
    ]
  ],
  "openalex": "W7212426150",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.01.26364553v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/26mcv721rd1wa.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:06Z",
      "native_id": "10.64898/2026.10.01.26364553",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.01.26364553v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "An interpretable Day-1 machine-learning model for COVID-19 mortality and severity prognosis: development in a Pakistani hospital cohort and external evaluation in Chinese and Italian populations",
  "title_text": "An interpretable Day-1 machine-learning model for COVID-19 mortality and severity prognosis: development in a Pakistani hospital cohort and external evaluation in Chinese and Italian populations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:06Z",
  "url": "https://research.draconitic.com/paper/26mcv721rd1wa",
  "venue": "medRxiv",
  "versions": []
}